A real Weave project that regression-tests three OpenAI models against the exact reply format your app depends on.

The post One Capital Letter Was Silently Breaking My AI Support Bot, and It Wasn't in the New Model appeared first on Towards Data Science.

Clarifying the math behind Anthropic’s representation workspace

The post Demystifying Anthropic's J-Space: A Mathematical Primer appeared first on Towards Data Science.

My first impressions of OpenAI's new frontier model

The post How to Maximize GPT-6 Astra appeared first on Towards Data Science.

A context window can be technically complete and still describe a world that no longer exists. I built a deterministic benchmark to measure the cost of acting on stale context.

The post Context Windows Don’t Know What’s Still True — I Built a Validity Layer That Does appeared first on Towards Data Science.

AI companies quietly watermark billions of words a day. Here’s how to apply the same three families of techniques to your own writing—and what real experiments reveal about which watermarks survive copy-paste, editing, and paraphrasing.

The post Text Watermarking in Python: Catch Whoever Copies Your Writing appeared first on Towards Data Science.

Three conditions that must hold before splitting prefill from decode pays off, and why chunked prefill is the right default below that threshold.

The post Disaggregation Is a Thousand-GPU Problem appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #B4] - A diagnostic and five composable operations, not a decision tree

The post Tables in PDFs for RAG: Don’t Flatten the Grid appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #B3] - A confident wrong answer is a bug. A bare “no answer” with no justification is almost as bad. Each of the four bricks has one piece of evidence to show

The post A RAG That Says “Not in This Document” Has to Show Four Kinds of Evidence appeared first on Towards Data Science.

Five failure modes that survive constrained decoding, and why your schema validator will never catch them.

The post Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch appeared first on Towards Data Science.

What I learned after thinking more carefully about Structured Outputs on messy, incomplete data

The post Your LLM Can Return Perfect JSON and Still Be Wrong appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #B2] - The FAQ inverts every brick of the standard RAG pipeline. Parsing is trivial, retrieval doubles as a cache, and few-shot prompting becomes a retrieval problem too

The post FAQ as RAG: When You Get to Design the Corpus appeared first on Towards Data Science.

A framework for building RAG pipelines that introduces complexity in response to observed failure modes, from lexical and hybrid search to reranking and agentic information seeking

The post Why RAG Complexity Should Be Earned appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #B1] - Three sources of one problem. User typos, fast-typing transcription noise, OCR character errors. Classical spell-check handles one of them. Embeddings carry the rest

The post Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #B00] - Retrieval answers one kind of question. Classifying a request, matching free text to a reference list, reading a table, cleaning OCR noise: each has a cheaper method that works, and the engineering is knowing which one to reach for

The post RAG Is Not the Whole Toolkit: The NLP Techniques Real Problems Still Need appeared first on Towards Data Science.

A hand-written CUDA inference runtime for Vision-Language-Action robots that decides what to remember, what to forget, and when it's simply too late to think.

The post Can an LLM Forget the Right Things? appeared first on Towards Data Science.

How DFlash trades spare compute for saved memory bandwidth, and why its gains shrink as concurrency rises

The post Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash appeared first on Towards Data Science.

Most LLM inference runtimes have no idea a physical deadline exists. This one refuses admission rather than miss a 33ms robot control cycle, evicts KV cache by meaning instead of age, and is written entirely in hand-written CUDA — no cuBLAS, no libtorch.

The post Can an LLM Forget the Right Things? appeared first on Towards Data Science.

Speculative decoding can turn underused CPU compute into faster token generation, without changing the model's output. In our vLLM tests, DFlash delivered 3.92x the autoregressive throughput with Qwen3.5-9B on Intel Xeon 6 at concurrency 1. We break down where the speedup comes from, explain the acceptance metrics, and show what determines whether speculation pays off.

The post Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash appeared first on Towards Data Science.

A hands-on guide to fine-tuning LLMs for the real world

The post How to Fine-Tune an LLM: An End-to-End Guide appeared first on Towards Data Science.

A controlled comparison of a top-5 RAG pipeline and a full 127,000 token prompt on the same 12 questions, same system prompt and same model. Graded blind on correctness, completeness and grounding.

The post Kimi K3’s 1M Token Context Window vs. RAG: Cost, Latency and Answer Quality appeared first on Towards Data Science.